system

Abstract

The system according to this embodiment aims to suggest gifts that suit the user's preferences at the appropriate time, and to handle everything from purchase to wrapping and message card creation in a consistent manner. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, an analysis unit, a purchase unit, a generation unit, and an explanation unit. The collection unit collects user information. The suggestion unit suggests gifts and timing based on the information collected by the collection unit. The analysis unit predicts the timing for purchasing the gifts suggested by the suggestion unit. The purchase unit purchases the products at the timing predicted by the analysis unit. The generation unit wraps the products purchased by the purchase unit. The explanation unit explains the wrapping and message card generated by the generation unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to propose a gift suitable for the user's preference at an appropriate timing and to consistently perform from purchase to wrapping and creation of a message card.

[0005] The system according to the embodiment aims to propose a gift suitable for the user's preference at an appropriate timing and to consistently perform from purchase to wrapping and creation of a message card.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, an analysis unit, a purchase unit, a generation unit, and an explanation unit. The collection unit collects user information. The suggestion unit suggests gifts and timing based on the information collected by the collection unit. The analysis unit predicts the timing for purchasing the gifts suggested by the suggestion unit. The purchase unit purchases the products at the timing predicted by the analysis unit. The generation unit wraps the products purchased by the purchase unit. The explanation unit explains the wrapping and message card generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest gifts that suit the user's preferences at the appropriate time and handle everything from purchase to wrapping and message card creation in a consistent manner. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The gift suggestion system according to an embodiment of the present invention is a system that suggests gifts that match the recipient's preferences for events such as Mother's Day and birthdays. This gift suggestion system uses AI to suggest gifts and timing based on information registered by the user on social media (such as birthdays) and initial registration information (such as family relationships and memories). Next, it also takes into account sales information from e-commerce sites to suggest purchasing the product at a time when it is most cost-effective. Furthermore, it creates gift wrapping and message card text according to the individual's hobbies and preferences. Finally, the AI ​​gift concierge explains the gift and its intention. For example, based on information registered by the user on social media (such as birthdays) and initial registration information (such as family relationships and memories), the AI ​​suggests gifts and timing. In this process, the AI ​​analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. For example, if it is known that the user's mother likes flowers on Mother's Day, the AI ​​suggests a bouquet of flowers. Next, it also takes into account sales information from e-commerce sites to suggest purchasing the product at a time when it is most cost-effective. The AI ​​learns about product price trends and sales information from past data to predict the optimal purchase timing. For example, it can predict when a specific product will go on sale and suggest purchasing it at that time. Furthermore, it can create gift wrapping and message card text tailored to the user's tastes and preferences. The AI ​​generates the optimal wrapping method and message card text based on the user's tastes and preferences. For example, if the user prefers a simple design, it will suggest simple wrapping and a message card. Finally, the AI ​​gift concierge explains the gift and its meaning. The AI ​​gift concierge explains the characteristics and meaning of the selected gift to the user, enhancing the value of the gift. For example, it conveys the intention of the gift by explaining the type of flower bouquet and its meaning. This system allows users to choose the perfect gift for someone even in their busy daily lives. It also prevents the gift selection from becoming monotonous and allows users to provide gifts that will please the recipient. In addition, integration with e-commerce sites allows for a smooth process from purchase to delivery.This allows the gift suggestion system to handle everything from suggesting gifts based on user information to purchasing, wrapping, and providing explanations.

[0029] The gift suggestion system according to this embodiment comprises a collection unit, a suggestion unit, an analysis unit, a purchase unit, a generation unit, and an explanation unit. The collection unit collects user information. For example, the collection unit collects the user's SNS registration information and initial registration information. The suggestion unit suggests gifts and timing based on the information collected by the collection unit. For example, the suggestion unit analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. The analysis unit predicts the timing of purchase for the gifts suggested by the suggestion unit. For example, the analysis unit analyzes sales information from e-commerce sites to predict the optimal purchase timing. The purchase unit purchases the products at the timing predicted by the analysis unit. For example, the purchase unit predicts when a particular product will go on sale and makes a purchase at that time. The generation unit wraps the products purchased by the purchase unit. For example, the generation unit generates gift wrapping methods according to the individual's hobbies and preferences. The explanation unit explains the wrapping and message card generated by the generation unit. For example, the explanation unit explains the selected gift product and its intent. As a result, the gift suggestion system according to this embodiment can consistently handle everything from suggesting gifts to purchasing, wrapping, and providing explanations, based on user information.

[0030] The data collection unit collects user information. For example, it collects user SNS registration information and initial registration information. Specifically, it collects data such as content posted by users on SNS, profile information, friendships, and pages and groups of interest. This allows for a detailed understanding of users' hobbies, interests, and social relationships. In addition, it collects basic information (name, age, gender, address, date of birth, etc.) that users enter when registering with the system as initial registration information. Furthermore, it collects information on products that users have purchased in the past and products they have viewed. This allows for an understanding of users' purchasing trends and preferences, enabling more accurate recommendations. The data collection unit centrally manages this information and can collaborate with other departments as needed. For example, the collected data is stored on a cloud server and made accessible to the proposal and analysis departments. By adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The suggestion department proposes gifts and timing based on information collected by the data collection department. For example, the suggestion department analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. Specifically, it analyzes the user's social media posts and purchase history to identify what kind of products the user is interested in. Furthermore, it considers important dates such as the user's birthday and anniversaries and proposes gifts that are timed accordingly. The suggestion department uses AI to analyze this data and select gifts that are best suited to the user's preferences and interests. For example, based on the trends of products the user has purchased in the past, it proposes products in the same category or related products. It can also propose products from specific brands or designers based on the user's hobbies and interests. Based on this information, the suggestion department proposes the optimal gift and the timing of its purchase to the user. In this way, the suggestion department can provide personalized suggestions that meet the user's needs and improve user satisfaction.

[0032] The analysis department predicts the optimal purchase timing for gifts proposed by the proposal department. For example, the analysis department analyzes sales information from e-commerce sites to predict the best purchase timing. Specifically, it analyzes past sales data and price fluctuation patterns from e-commerce sites to predict when a particular product will be at its lowest price. Furthermore, it considers sales information related to seasons and events to identify the most advantageous purchase timing for the user. The analysis department uses AI to analyze this data and simulate multiple scenarios to identify the most likely purchase timing. For example, if a particular product is likely to be discounted during major sales such as Black Friday or Cyber ​​Monday, it will recommend purchasing it at that time. In addition, the analysis department can continuously revise its prediction results based on real-time updated data to respond to the latest situation. As a result, the analysis department can provide users with the optimal purchase timing and realize cost-effective gifts.

[0033] The purchasing unit purchases products at times predicted by the analysis unit. For example, the purchasing unit predicts when a specific product will go on sale and makes a purchase at that time. Specifically, it uses the API of e-commerce sites to automatically initiate the purchase process when a specific product goes on sale. The purchasing unit also compares multiple e-commerce sites to purchase products at the lowest price. By pre-registering the user's payment information and shipping address information, the purchasing unit can process purchases quickly and efficiently. Furthermore, the purchasing unit manages the purchase history and notifies the user of purchase confirmations and shipping status. In this way, the purchasing unit provides users with a smooth purchasing experience and allows them to efficiently prepare gifts.

[0034] The generation unit wraps the products purchased by the purchase unit. For example, the generation unit generates gift wrapping methods tailored to the individual's tastes and preferences. Specifically, it selects the color and design of the wrapping paper and ribbon according to the user's preferences and the theme of the gift. Furthermore, the generation unit can use AI to automatically generate wrapping designs and suggest multiple wrapping options to the user. For example, based on the theme chosen by the user, it can suggest seasonal designs or designs based on specific characters. In addition, the generation unit can perform wrapping work efficiently by automating the wrapping process. As a result, the generation unit can provide users with high-quality, personalized wrapping, enhancing the value of the gift.

[0035] The description unit explains the wrapping and message card generated by the generation unit. For example, the description unit explains the selected gift item and its intent. Specifically, it provides a detailed explanation of the gift's background, selection reasons, and the message it conveys. Furthermore, the description unit can use AI to generate message card content tailored to the user's preferences and interests. For example, if the user prefers a particular theme or style, it can generate a message that matches that theme and include it with the gift. The description unit can also provide practical information such as how to use and maintain the gift. This allows the description unit to enhance the value of the gift and create a memorable experience for the recipient. In addition, the description unit can collect user feedback and continuously improve the message content and explanation methods. This allows the description unit to consistently provide high-quality explanations and improve user satisfaction.

[0036] The data collection unit can collect users' SNS registration information and initial registration information. For example, the data collection unit collects users' SNS registration information. For example, the data collection unit collects users' initial registration information. For example, the data collection unit collects users' SNS registration information and initial registration information in combination. By collecting users' SNS registration information and initial registration information, it becomes possible to provide more personalized suggestions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input users' SNS registration information into AI, and the AI ​​can analyze and collect the information.

[0037] The suggestion unit can propose gifts and their timing based on the information collected by the collection unit. For example, the suggestion unit proposes gifts based on the information collected by the collection unit. For example, the suggestion unit proposes the timing of gifts based on the information collected by the collection unit. For example, the suggestion unit proposes a combination of gifts and their timing based on the information collected by the collection unit. This allows the suggestion unit to propose the optimal gift and its timing based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the information collected by the collection unit into an AI, which can then propose gifts and their timing.

[0038] The analysis unit can analyze sales information from e-commerce sites and predict the optimal purchase timing. For example, the analysis unit analyzes sales information from e-commerce sites. For example, the analysis unit predicts the optimal purchase timing based on sales information from e-commerce sites. For example, the analysis unit analyzes sales information from e-commerce sites and predicts the purchase timing. In this way, by analyzing sales information, the optimal purchase timing can be predicted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sales information from e-commerce sites into AI, and the AI ​​can predict the optimal purchase timing.

[0039] The generation unit can generate gift wrapping methods tailored to an individual's hobbies and preferences. For example, the generation unit generates wrapping methods tailored to an individual's hobbies. For example, the generation unit generates wrapping methods tailored to an individual's preferences. For example, the generation unit generates wrapping methods based on an individual's hobbies and preferences. This makes it possible to create more personalized gifts by generating wrapping methods tailored to an individual's hobbies and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about an individual's hobbies and preferences into an AI, which can then generate wrapping methods.

[0040] The generation unit can generate text for message cards. For example, the generation unit generates text for message cards. For example, the generation unit generates text for message cards based on personal hobbies and preferences. For example, the generation unit generates text for message cards to match a specific event. In this way, by generating text for message cards, it is possible to automatically create a message to accompany a gift. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input text for message cards into AI, and the AI ​​can generate the text.

[0041] The explanation unit can explain the selected gift item and its intent. For example, the explanation unit can explain the selected gift item. For example, the explanation unit can explain the selected gift's intent. For example, the explanation unit can explain the selected gift item and its intent in combination. This enhances the perceived value of the gift by explaining the selected gift item and its intent. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the selected gift item and intent into AI, which can then provide the explanation.

[0042] The data collection unit can analyze a user's past SNS activity and select the optimal information collection method. For example, the data collection unit can analyze the times when a user frequently posts and collect information during those times. For example, if a user frequently uses a particular hashtag, the data collection unit will prioritize collecting information related to that hashtag. For example, if a user frequently uses a particular SNS platform, the data collection unit will prioritize collecting information from that platform. In this way, the optimal information collection method can be selected by analyzing a user's past SNS activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past SNS activity data into AI, which can then select the optimal information collection method.

[0043] The data collection unit can filter information based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit will prioritize collecting information related to that hobby. For example, if a user moves, the data collection unit will collect information related to the new area. For example, if a user plans to attend a specific event, the data collection unit will collect information related to that event. By filtering information based on the user's current lifestyle and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into the AI, which can then filter the information.

[0044] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is traveling, the data collection unit will prioritize the collection of information related to their travel destination. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, if the user plans to attend a specific event, the data collection unit will prioritize the collection of information related to the event's location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant information.

[0045] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, if a user frequently posts about a particular topic, the data collection unit can collect information related to that topic. For example, if a user belongs to a particular group, the data collection unit can collect information related to that group. For example, if a user plans to attend a particular event, the data collection unit can collect information related to that event. In this way, relevant information can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant information.

[0046] The suggestion function can adjust the level of detail in a suggestion based on the importance of the gift. For example, for an important event, the suggestion function will provide a suggestion with detailed information. For an everyday gift, the suggestion function will provide a concise suggestion. For a special anniversary, the suggestion function will provide a special suggestion. By adjusting the level of detail in the suggestion function based on the importance of the gift, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input gift importance data into the AI, which can then adjust the level of detail in the suggestion.

[0047] The suggestion unit can apply different suggestion algorithms depending on the gift category when making suggestions. For example, in the case of a bouquet, the suggestion unit applies an algorithm that suggests flower types and color combinations. For example, in the case of accessories, the suggestion unit applies an algorithm that suggests based on design and materials. For example, in the case of food, the suggestion unit applies an algorithm that suggests based on taste and expiration date. By applying different suggestion algorithms depending on the gift category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input gift category data into the AI, and the AI ​​can apply different suggestion algorithms.

[0048] The proposal department can prioritize proposals based on the timing of gift submission. For example, if an important event is approaching, the proposal department will prioritize proposals related to that event. For example, if an everyday gift is being submitted, the proposal department will give a lower priority to that proposal than to others. For example, if a special anniversary is being celebrated, the proposal department will prioritize proposals related to that anniversary. This allows for more appropriate proposals to be made by prioritizing proposals based on the timing of gift submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input gift submission timing data into AI, which can then determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion unit may prioritize suggesting gifts related to the user's hobbies and interests. For example, the suggestion unit may prioritize suggesting gifts that are highly relevant based on the user's past purchase history. For example, the suggestion unit may prioritize suggesting gifts related to the user's family relationships or memories. By adjusting the order of suggestions based on the relevance of the gifts, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input gift relevance data into AI, and the AI ​​can adjust the order of suggestions.

[0050] The analysis unit can predict the optimal purchase timing by referring to past sales information during analysis. For example, the analysis unit can predict when a specific product will go on sale based on past sales information. For example, the analysis unit can analyze the price trends of a product based on past sales information and predict the optimal purchase timing. For example, the analysis unit can predict when a product related to a specific event will go on sale based on past sales information. In this way, the optimal purchase timing can be predicted by referring to past sales information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past sales information into AI, and the AI ​​can predict the optimal purchase timing.

[0051] The analysis unit can apply different analysis methods to each gift category during analysis. For example, in the case of a bouquet, the analysis unit applies a method that analyzes the types and color combinations of flowers. For example, in the case of accessories, the analysis unit applies a method that analyzes based on the design and materials. For example, in the case of food, the analysis unit applies a method that analyzes based on the taste and expiration date. By applying different analysis methods to each gift category, a more appropriate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input gift category data into the AI, and the AI ​​can apply different analysis methods.

[0052] The analysis unit can perform analysis while considering the geographical distribution of gifts. For example, the analysis unit can analyze the optimal delivery method while considering the geographical distribution of gift destinations. For example, the analysis unit can analyze the optimal delivery timing while considering the geographical distribution of gift destinations. For example, the analysis unit can analyze the optimal delivery route while considering the geographical distribution of gift destinations. This allows for more appropriate analysis by considering the geographical distribution of gifts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of gifts into AI, and the AI ​​can perform the analysis.

[0053] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on gifts during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers related to gifts. For example, the analysis unit can improve the accuracy of its analysis by referring to patent documents related to gifts. For example, the analysis unit can improve the accuracy of its analysis by referring to market research reports related to gifts. In this way, the accuracy of the analysis can be improved by referring to relevant literature on gifts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on relevant literature on gifts into AI, which can then improve the accuracy of the analysis.

[0054] The purchasing department can analyze a user's past purchase history to select the optimal purchasing method at the time of purchase. For example, the purchasing department may prioritize suggesting purchasing methods the user has used in the past (credit card, electronic money, etc.). For example, the purchasing department may analyze the trends of products the user has purchased in the past and suggest similar products. For example, the purchasing department may prioritize suggesting specific brands or stores based on the user's past purchase history. In this way, the optimal purchasing method can be selected by analyzing the user's past purchase history. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input the user's past purchase history data into AI, and the AI ​​can select the optimal purchasing method.

[0055] The purchasing unit can customize the purchase method based on the user's current lifestyle at the time of purchase. For example, if the user is busy, the purchasing unit will suggest a quick purchase method. If the user is relaxed, the purchasing unit will guide the user through the purchase process while providing detailed information. If the user is traveling, the purchasing unit will suggest a method of receiving the purchase at their travel destination. By customizing the purchase method based on the user's current lifestyle, a more appropriate purchase method can be provided. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or not. For example, the purchasing unit can input the user's current lifestyle data into the AI, which can then customize the purchase method.

[0056] The purchasing unit can select the optimal purchase method at the time of purchase, taking into account the user's geographical location information. For example, if the user is in a specific region, the purchasing unit can suggest a purchase method available in that region. For example, if the user is traveling, the purchasing unit can suggest a method of receiving the purchase at their travel destination. For example, if the user is near a specific store, the purchasing unit can suggest a purchase method at that store. In this way, the optimal purchase method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or without AI. For example, the purchasing unit can input the user's geographical location information into AI, and the AI ​​can select the optimal purchase method.

[0057] The purchasing department can analyze a user's social media activity and suggest purchasing methods at the time of purchase. For example, if a user frequently posts about a particular brand or store, the purchasing department will suggest purchasing methods from that brand or store. For example, if a user frequently posts reviews of a particular product, the purchasing department will prioritize suggesting that product. For example, if a user plans to attend a particular event, the purchasing department will suggest purchasing methods for products related to that event. In this way, by analyzing a user's social media activity, the optimal purchasing method can be suggested. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input user social media activity data into AI, and the AI ​​can suggest purchasing methods.

[0058] The generation unit can select the optimal wrapping method by referring to the user's past wrapping history when generating wrapping. For example, the generation unit may suggest a similar design based on a wrapping design the user has previously preferred. For example, the generation unit may suggest similar materials based on wrapping materials the user has previously used. For example, the generation unit may suggest a wrapping method suitable for a specific event based on the user's past wrapping history. In this way, the optimal wrapping method can be selected by referring to the user's past wrapping history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past wrapping history data into AI, and the AI ​​can select the optimal wrapping method.

[0059] The generation unit can customize the wrapping based on the user's current hobbies and preferences when generating the wrapping. For example, if the user has started a new hobby, the generation unit will suggest a wrapping design related to that hobby. For example, if the user likes a particular color, the generation unit will suggest a wrapping based on that color. For example, if the user likes a particular theme, the generation unit will suggest a wrapping based on that theme. This allows for more personalized wrapping by customizing the wrapping based on the user's current hobbies and preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's current hobbies and preferences into the AI, which can then customize the wrapping.

[0060] The generation unit can select the optimal wrapping method by considering the user's geographical location information when generating wrapping. For example, if the user is in a specific region, the generation unit will suggest wrapping that is appropriate for the culture and customs of that region. For example, if the user is traveling, the generation unit will suggest wrapping that is suitable for receiving the item at their travel destination. For example, if the user is planning to attend a specific event, the generation unit will suggest wrapping that is related to that event. In this way, the optimal wrapping method can be selected by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into the AI, and the AI ​​can select the optimal wrapping method.

[0061] The generation unit can analyze the user's social media activity and suggest wrapping options when generating wrapping. For example, if the user frequently posts about a particular design or theme, the generation unit will suggest wrapping based on that design or theme. For example, if the user plans to attend a particular event, the generation unit will suggest wrapping related to that event. For example, if the user frequently posts about a particular brand or store, the generation unit will suggest wrapping for that brand or store. In this way, by analyzing the user's social media activity, the optimal wrapping option can be suggested. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into AI, which can then suggest wrapping options.

[0062] The explanation unit can select the optimal explanation method by referring to the user's past explanation history during explanation. For example, the explanation unit can explain using a similar method based on the explanation method the user has preferred in the past. For example, the explanation unit can explain using a similar method based on the explanation method the user has found easy to understand in the past. For example, the explanation unit can suggest an explanation method suitable for a specific event based on the user's past explanation history. In this way, the optimal explanation method can be selected by referring to the user's past explanation history. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's past explanation history data into AI, and the AI ​​can select the optimal explanation method.

[0063] The explanation unit can customize its explanations based on the user's current knowledge level. For example, if the user is a beginner, the explanation unit will focus on basic information. If the user is an intermediate user, the explanation unit will include detailed information. If the user is an advanced user, the explanation unit will include specialized information. This allows for more appropriate explanations by customizing them based on the user's current knowledge level. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input user knowledge level data into AI, which can then customize the explanation.

[0064] The explanation unit can select the most appropriate explanation method by considering the user's geographical location information during the explanation process. For example, if the user is in a specific region, the explanation unit will provide an explanation tailored to the culture and customs of that region. For example, if the user is traveling, the explanation unit will provide an explanation suitable for receiving the product at their travel destination. For example, if the user is planning to attend a specific event, the explanation unit will provide an explanation related to that event. In this way, the explanation unit can select the most appropriate explanation method by considering the user's geographical location information. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's geographical location information into AI, which can then select the most appropriate explanation method.

[0065] The explanation unit can analyze the user's social media activity and suggest explanation methods during the explanation process. For example, if the user frequently posts about a particular topic, the explanation unit will provide an explanation related to that topic. For example, if the user belongs to a particular group, the explanation unit will provide an explanation related to that group. For example, if the user plans to attend a particular event, the explanation unit will provide an explanation related to that event. In this way, by analyzing the user's social media activity, the explanation unit can suggest the most suitable explanation method. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can input the user's social media activity data into AI, and the AI ​​can suggest explanation methods.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The gift suggestion system can analyze a user's past purchase history and suggest the most suitable gift. For example, it can analyze the trends of products a user has purchased in the past and suggest similar products. If a user prefers a particular brand, it can suggest products from that brand. Also, if a user frequently purchases products from a particular category, it can suggest products from that category. In this way, the system can suggest the most suitable gift based on the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past purchase history data into the AI, which can then suggest the most suitable gift.

[0068] The gift suggestion system can customize gift suggestions based on the user's current lifestyle. For example, if a user starts a new hobby, the system can suggest gifts related to that hobby. If a user moves, the system can suggest gifts related to their new area. Furthermore, if a user plans to attend a specific event, the system can suggest gifts related to that event. This allows the system to suggest the most suitable gift based on the user's current lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's current lifestyle data into the AI, which can then customize the gift suggestions.

[0069] The gift suggestion system can suggest gifts while considering the user's geographical location. For example, if the user is in a specific region, it can suggest gifts that are appropriate for the culture and customs of that region. If the user is traveling, it can suggest gifts that are suitable for receiving at their destination. Furthermore, if the user is planning to attend a specific event, it can suggest gifts related to that event. This allows the system to suggest the most suitable gift while considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into the AI, which can then suggest the most suitable gift.

[0070] The gift suggestion system can analyze a user's social media activity and suggest the most suitable gift. For example, if a user frequently posts about a particular topic, it can suggest a gift related to that topic. If a user belongs to a particular group, it can suggest a gift related to that group. Furthermore, if a user plans to attend a particular event, it can suggest a gift related to that event. This allows the system to analyze a user's social media activity and suggest the most suitable gift. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's social media activity data into an AI, which can then suggest the most suitable gift.

[0071] The gift suggestion system can analyze a user's past social media activity and select the most suitable gift suggestion method. For example, it can analyze the times when a user frequently posts and suggest gifts during those times. If a user frequently uses a particular hashtag, it can prioritize suggesting gifts related to that hashtag. Furthermore, if a user frequently uses a particular social media platform, it can suggest gifts based on information from that platform. This allows the system to analyze a user's past social media activity and select the most suitable gift suggestion method. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past social media activity data into an AI, which can then select the most suitable gift suggestion method.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit collects user information. For example, the collection unit collects the user's SNS registration information and initial registration information. Step 2: The suggestion department proposes gifts and timing based on the information collected by the data collection department. For example, the suggestion department analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. Step 3: The analysis unit predicts the optimal timing for purchasing the gift suggested by the proposal unit. For example, the analysis unit analyzes sales information from e-commerce sites to predict the best time to purchase. Step 4: The purchasing unit purchases the product at the timing predicted by the analysis unit. For example, the purchasing unit predicts when a particular product will go on sale and makes a purchase at that time. Step 5: The generation unit wraps the products purchased by the purchase unit. For example, the generation unit generates gift wrapping methods tailored to the individual's tastes and preferences. Step 6: The description section describes the wrapping and message card generated by the generation section. The description section describes, for example, the selected gift item and its meaning.

[0074] (Example of form 2) The gift suggestion system according to an embodiment of the present invention is a system that suggests gifts that match the recipient's preferences for events such as Mother's Day and birthdays. This gift suggestion system uses AI to suggest gifts and timing based on information registered by the user on social media (such as birthdays) and initial registration information (such as family relationships and memories). Next, it also takes into account sales information from e-commerce sites to suggest purchasing the product at a time when it is most cost-effective. Furthermore, it creates gift wrapping and message card text according to the individual's hobbies and preferences. Finally, the AI ​​gift concierge explains the gift and its intention. For example, based on information registered by the user on social media (such as birthdays) and initial registration information (such as family relationships and memories), the AI ​​suggests gifts and timing. In this process, the AI ​​analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. For example, if it is known that the user's mother likes flowers on Mother's Day, the AI ​​suggests a bouquet of flowers. Next, it also takes into account sales information from e-commerce sites to suggest purchasing the product at a time when it is most cost-effective. The AI ​​learns about product price trends and sales information from past data to predict the optimal purchase timing. For example, it can predict when a specific product will go on sale and suggest purchasing it at that time. Furthermore, it can create gift wrapping and message card text tailored to the user's tastes and preferences. The AI ​​generates the optimal wrapping method and message card text based on the user's tastes and preferences. For example, if the user prefers a simple design, it will suggest simple wrapping and a message card. Finally, the AI ​​gift concierge explains the gift and its meaning. The AI ​​gift concierge explains the characteristics and meaning of the selected gift to the user, enhancing the value of the gift. For example, it conveys the intention of the gift by explaining the type of flower bouquet and its meaning. This system allows users to choose the perfect gift for someone even in their busy daily lives. It also prevents the gift selection from becoming monotonous and allows users to provide gifts that will please the recipient. In addition, integration with e-commerce sites allows for a smooth process from purchase to delivery.This allows the gift suggestion system to handle everything from suggesting gifts based on user information to purchasing, wrapping, and providing explanations.

[0075] The gift suggestion system according to this embodiment comprises a collection unit, a suggestion unit, an analysis unit, a purchase unit, a generation unit, and an explanation unit. The collection unit collects user information. For example, the collection unit collects the user's SNS registration information and initial registration information. The suggestion unit suggests gifts and timing based on the information collected by the collection unit. For example, the suggestion unit analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. The analysis unit predicts the timing of purchase for the gifts suggested by the suggestion unit. For example, the analysis unit analyzes sales information from e-commerce sites to predict the optimal purchase timing. The purchase unit purchases the products at the timing predicted by the analysis unit. For example, the purchase unit predicts when a particular product will go on sale and makes a purchase at that time. The generation unit wraps the products purchased by the purchase unit. For example, the generation unit generates gift wrapping methods according to the individual's hobbies and preferences. The explanation unit explains the wrapping and message card generated by the generation unit. For example, the explanation unit explains the selected gift product and its intent. As a result, the gift suggestion system according to this embodiment can consistently handle everything from suggesting gifts to purchasing, wrapping, and providing explanations, based on user information.

[0076] The data collection unit collects user information. For example, it collects user SNS registration information and initial registration information. Specifically, it collects data such as content posted by users on SNS, profile information, friendships, and pages and groups of interest. This allows for a detailed understanding of users' hobbies, interests, and social relationships. In addition, it collects basic information (name, age, gender, address, date of birth, etc.) that users enter when registering with the system as initial registration information. Furthermore, it collects information on products that users have purchased in the past and products they have viewed. This allows for an understanding of users' purchasing trends and preferences, enabling more accurate recommendations. The data collection unit centrally manages this information and can collaborate with other departments as needed. For example, the collected data is stored on a cloud server and made accessible to the proposal and analysis departments. By adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0077] The suggestion department proposes gifts and timing based on information collected by the data collection department. For example, the suggestion department analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. Specifically, it analyzes the user's social media posts and purchase history to identify what kind of products the user is interested in. Furthermore, it considers important dates such as the user's birthday and anniversaries and proposes gifts that are timed accordingly. The suggestion department uses AI to analyze this data and select gifts that are best suited to the user's preferences and interests. For example, based on the trends of products the user has purchased in the past, it proposes products in the same category or related products. It can also propose products from specific brands or designers based on the user's hobbies and interests. Based on this information, the suggestion department proposes the optimal gift and the timing of its purchase to the user. In this way, the suggestion department can provide personalized suggestions that meet the user's needs and improve user satisfaction.

[0078] The analysis department predicts the optimal purchase timing for gifts proposed by the proposal department. For example, the analysis department analyzes sales information from e-commerce sites to predict the best purchase timing. Specifically, it analyzes past sales data and price fluctuation patterns from e-commerce sites to predict when a particular product will be at its lowest price. Furthermore, it considers sales information related to seasons and events to identify the most advantageous purchase timing for the user. The analysis department uses AI to analyze this data and simulate multiple scenarios to identify the most likely purchase timing. For example, if a particular product is likely to be discounted during major sales such as Black Friday or Cyber ​​Monday, it will recommend purchasing it at that time. In addition, the analysis department can continuously revise its prediction results based on real-time updated data to respond to the latest situation. As a result, the analysis department can provide users with the optimal purchase timing and realize cost-effective gifts.

[0079] The purchasing unit purchases products at times predicted by the analysis unit. For example, the purchasing unit predicts when a specific product will go on sale and makes a purchase at that time. Specifically, it uses the API of e-commerce sites to automatically initiate the purchase process when a specific product goes on sale. The purchasing unit also compares multiple e-commerce sites to purchase products at the lowest price. By pre-registering the user's payment information and shipping address information, the purchasing unit can process purchases quickly and efficiently. Furthermore, the purchasing unit manages the purchase history and notifies the user of purchase confirmations and shipping status. In this way, the purchasing unit provides users with a smooth purchasing experience and allows them to efficiently prepare gifts.

[0080] The generation unit wraps the products purchased by the purchase unit. For example, the generation unit generates gift wrapping methods tailored to the individual's tastes and preferences. Specifically, it selects the color and design of the wrapping paper and ribbon according to the user's preferences and the theme of the gift. Furthermore, the generation unit can use AI to automatically generate wrapping designs and suggest multiple wrapping options to the user. For example, based on the theme chosen by the user, it can suggest seasonal designs or designs based on specific characters. In addition, the generation unit can perform wrapping work efficiently by automating the wrapping process. As a result, the generation unit can provide users with high-quality, personalized wrapping, enhancing the value of the gift.

[0081] The description unit explains the wrapping and message card generated by the generation unit. For example, the description unit explains the selected gift item and its intent. Specifically, it provides a detailed explanation of the gift's background, selection reasons, and the message it conveys. Furthermore, the description unit can use AI to generate message card content tailored to the user's preferences and interests. For example, if the user prefers a particular theme or style, it can generate a message that matches that theme and include it with the gift. The description unit can also provide practical information such as how to use and maintain the gift. This allows the description unit to enhance the value of the gift and create a memorable experience for the recipient. In addition, the description unit can collect user feedback and continuously improve the message content and explanation methods. This allows the description unit to consistently provide high-quality explanations and improve user satisfaction.

[0082] The data collection unit can collect users' SNS registration information and initial registration information. For example, the data collection unit collects users' SNS registration information. For example, the data collection unit collects users' initial registration information. For example, the data collection unit collects users' SNS registration information and initial registration information in combination. By collecting users' SNS registration information and initial registration information, it becomes possible to provide more personalized suggestions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input users' SNS registration information into AI, and the AI ​​can analyze and collect the information.

[0083] The suggestion unit can propose gifts and their timing based on the information collected by the collection unit. For example, the suggestion unit proposes gifts based on the information collected by the collection unit. For example, the suggestion unit proposes the timing of gifts based on the information collected by the collection unit. For example, the suggestion unit proposes a combination of gifts and their timing based on the information collected by the collection unit. This allows the suggestion unit to propose the optimal gift and its timing based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the information collected by the collection unit into an AI, which can then propose gifts and their timing.

[0084] The analysis unit can analyze sales information from e-commerce sites and predict the optimal purchase timing. For example, the analysis unit analyzes sales information from e-commerce sites. For example, the analysis unit predicts the optimal purchase timing based on sales information from e-commerce sites. For example, the analysis unit analyzes sales information from e-commerce sites and predicts the purchase timing. In this way, by analyzing sales information, the optimal purchase timing can be predicted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input sales information from e-commerce sites into AI, and the AI ​​can predict the optimal purchase timing.

[0085] The generation unit can generate gift wrapping methods tailored to an individual's hobbies and preferences. For example, the generation unit generates wrapping methods tailored to an individual's hobbies. For example, the generation unit generates wrapping methods tailored to an individual's preferences. For example, the generation unit generates wrapping methods based on an individual's hobbies and preferences. This makes it possible to create more personalized gifts by generating wrapping methods tailored to an individual's hobbies and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information about an individual's hobbies and preferences into an AI, which can then generate wrapping methods.

[0086] The generation unit can generate text for message cards. For example, the generation unit generates text for message cards. For example, the generation unit generates text for message cards based on personal hobbies and preferences. For example, the generation unit generates text for message cards to match a specific event. In this way, by generating text for message cards, it is possible to automatically create a message to accompany a gift. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input text for message cards into AI, and the AI ​​can generate the text.

[0087] The explanation unit can explain the selected gift item and its intent. For example, the explanation unit can explain the selected gift item. For example, the explanation unit can explain the selected gift's intent. For example, the explanation unit can explain the selected gift item and its intent in combination. This enhances the perceived value of the gift by explaining the selected gift item and its intent. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the selected gift item and intent into AI, which can then provide the explanation.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. For example, if the user is busy, the data collection unit can collect information at night or on weekends to reduce the user's burden. For example, if the user is emotionally stable, the data collection unit can collect information regularly to always maintain up-to-date information. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of information collection.

[0089] The data collection unit can analyze a user's past SNS activity and select the optimal information collection method. For example, the data collection unit can analyze the times when a user frequently posts and collect information during those times. For example, if a user frequently uses a particular hashtag, the data collection unit will prioritize collecting information related to that hashtag. For example, if a user frequently uses a particular SNS platform, the data collection unit will prioritize collecting information from that platform. In this way, the optimal information collection method can be selected by analyzing a user's past SNS activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past SNS activity data into AI, which can then select the optimal information collection method.

[0090] The data collection unit can filter information based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the data collection unit will prioritize collecting information related to that hobby. For example, if a user moves, the data collection unit will collect information related to the new area. For example, if a user plans to attend a specific event, the data collection unit will collect information related to that event. By filtering information based on the user's current lifestyle and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into the AI, which can then filter the information.

[0091] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is emotionally unstable, the data collection unit will prioritize collecting positive information. For example, if the user is relaxed, the data collection unit will prioritize collecting interesting information. For example, if the user is busy, the data collection unit will prioritize collecting important information. This allows for the collection of more relevant information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of information.

[0092] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, if the user is traveling, the data collection unit will prioritize the collection of information related to their travel destination. For example, if the user lives in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, if the user plans to attend a specific event, the data collection unit will prioritize the collection of information related to the event's location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant information.

[0093] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, if a user frequently posts about a particular topic, the data collection unit can collect information related to that topic. For example, if a user belongs to a particular group, the data collection unit can collect information related to that group. For example, if a user plans to attend a particular event, the data collection unit can collect information related to that event. In this way, relevant information can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, and the AI ​​can collect relevant information.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is emotionally unstable, the suggestion unit will present suggestions in gentle language. If the user is relaxed, the suggestion unit will present suggestions that include detailed information. If the user is in a hurry, the suggestion unit will present concise and to-the-point suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the way suggestions are presented.

[0095] The suggestion function can adjust the level of detail in a suggestion based on the importance of the gift. For example, for an important event, the suggestion function will provide a suggestion with detailed information. For an everyday gift, the suggestion function will provide a concise suggestion. For a special anniversary, the suggestion function will provide a special suggestion. By adjusting the level of detail in the suggestion function based on the importance of the gift, more appropriate suggestions can be made. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not using AI. For example, the suggestion function can input gift importance data into the AI, which can then adjust the level of detail in the suggestion.

[0096] The suggestion unit can apply different suggestion algorithms depending on the gift category when making suggestions. For example, in the case of a bouquet, the suggestion unit applies an algorithm that suggests flower types and color combinations. For example, in the case of accessories, the suggestion unit applies an algorithm that suggests based on design and materials. For example, in the case of food, the suggestion unit applies an algorithm that suggests based on taste and expiration date. By applying different suggestion algorithms depending on the gift category, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input gift category data into the AI, and the AI ​​can apply different suggestion algorithms.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit will make a longer suggestion with more detailed information. If the user is emotionally unstable, the suggestion unit will make a short suggestion using gentle language. By adjusting the length of the suggestion based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the length of the suggestion.

[0098] The proposal department can prioritize proposals based on the timing of gift submission. For example, if an important event is approaching, the proposal department will prioritize proposals related to that event. For example, if an everyday gift is being submitted, the proposal department will give a lower priority to that proposal than to others. For example, if a special anniversary is being celebrated, the proposal department will prioritize proposals related to that anniversary. This allows for more appropriate proposals to be made by prioritizing proposals based on the timing of gift submission. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input gift submission timing data into AI, which can then determine the priority of proposals.

[0099] The suggestion unit can adjust the order of suggestions based on the relevance of the gifts when making suggestions. For example, the suggestion unit may prioritize suggesting gifts related to the user's hobbies and interests. For example, the suggestion unit may prioritize suggesting gifts that are highly relevant based on the user's past purchase history. For example, the suggestion unit may prioritize suggesting gifts related to the user's family relationships or memories. By adjusting the order of suggestions based on the relevance of the gifts, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input gift relevance data into AI, and the AI ​​can adjust the order of suggestions.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is emotionally unstable, the analysis unit will prioritize analyzing positive information. For example, if the user is relaxed, the analysis unit will perform an analysis that includes detailed information. For example, if the user is in a hurry, the analysis unit will perform a concise and to-the-point analysis. This allows for more appropriate analysis by adjusting the analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into an AI, which can then adjust the analysis criteria.

[0101] The analysis unit can predict the optimal purchase timing by referring to past sales information during analysis. For example, the analysis unit can predict when a specific product will go on sale based on past sales information. For example, the analysis unit can analyze the price trends of a product based on past sales information and predict the optimal purchase timing. For example, the analysis unit can predict when a product related to a specific event will go on sale based on past sales information. In this way, the optimal purchase timing can be predicted by referring to past sales information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past sales information into AI, and the AI ​​can predict the optimal purchase timing.

[0102] The analysis unit can apply different analysis methods to each gift category during analysis. For example, in the case of a bouquet, the analysis unit applies a method that analyzes the types and color combinations of flowers. For example, in the case of accessories, the analysis unit applies a method that analyzes based on the design and materials. For example, in the case of food, the analysis unit applies a method that analyzes based on the taste and expiration date. By applying different analysis methods to each gift category, a more appropriate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input gift category data into the AI, and the AI ​​can apply different analysis methods.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is emotionally unstable, the analysis unit will prioritize displaying positive information. For example, if the user is relaxed, the analysis unit will provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit will provide a concise and to-the-point display method. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the display method of the analysis results.

[0104] The analysis unit can perform analysis while considering the geographical distribution of gifts. For example, the analysis unit can analyze the optimal delivery method while considering the geographical distribution of gift destinations. For example, the analysis unit can analyze the optimal delivery timing while considering the geographical distribution of gift destinations. For example, the analysis unit can analyze the optimal delivery route while considering the geographical distribution of gift destinations. This allows for more appropriate analysis by considering the geographical distribution of gifts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of gifts into AI, and the AI ​​can perform the analysis.

[0105] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on gifts during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to academic papers related to gifts. For example, the analysis unit can improve the accuracy of its analysis by referring to patent documents related to gifts. For example, the analysis unit can improve the accuracy of its analysis by referring to market research reports related to gifts. In this way, the accuracy of the analysis can be improved by referring to relevant literature on gifts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on relevant literature on gifts into AI, which can then improve the accuracy of the analysis.

[0106] The purchasing unit can estimate the user's emotions and adjust the timing of the purchase based on the estimated emotions. For example, if the user is emotionally unstable, the purchasing unit may delay the purchase. For example, if the user is relaxed, the purchasing unit may speed up the purchase. For example, if the user is in a hurry, the purchasing unit may make the purchase quickly. In this way, by adjusting the timing of the purchase based on the user's emotions, purchases can be made at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing unit may be performed using AI or not using AI. For example, the purchasing unit can input user emotion data into an AI, and the AI ​​can adjust the timing of the purchase.

[0107] The purchasing department can analyze a user's past purchase history to select the optimal purchasing method at the time of purchase. For example, the purchasing department may prioritize suggesting purchasing methods the user has used in the past (credit card, electronic money, etc.). For example, the purchasing department may analyze the trends of products the user has purchased in the past and suggest similar products. For example, the purchasing department may prioritize suggesting specific brands or stores based on the user's past purchase history. In this way, the optimal purchasing method can be selected by analyzing the user's past purchase history. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not using AI. For example, the purchasing department can input the user's past purchase history data into AI, and the AI ​​can select the optimal purchasing method.

[0108] The purchasing unit can customize the purchase method based on the user's current lifestyle at the time of purchase. For example, if the user is busy, the purchasing unit will suggest a quick purchase method. If the user is relaxed, the purchasing unit will guide the user through the purchase process while providing detailed information. If the user is traveling, the purchasing unit will suggest a method of receiving the purchase at their travel destination. By customizing the purchase method based on the user's current lifestyle, a more appropriate purchase method can be provided. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or not. For example, the purchasing unit can input the user's current lifestyle data into the AI, which can then customize the purchase method.

[0109] The purchasing unit can estimate the user's emotions and determine purchase priorities based on those estimated emotions. For example, if the user is emotionally unstable, the purchasing unit will prioritize important purchases. If the user is relaxed, the purchasing unit will prioritize everyday purchases. If the user is in a hurry, the purchasing unit will prioritize items that can be purchased quickly. This allows for more appropriate purchases by determining purchase priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing unit may be performed using AI or not. For example, the purchasing unit can input user emotion data into an AI, which can then determine purchase priorities.

[0110] The purchasing unit can select the optimal purchase method at the time of purchase, taking into account the user's geographical location information. For example, if the user is in a specific region, the purchasing unit can suggest a purchase method available in that region. For example, if the user is traveling, the purchasing unit can suggest a method of receiving the purchase at their travel destination. For example, if the user is near a specific store, the purchasing unit can suggest a purchase method at that store. In this way, the optimal purchase method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or without AI. For example, the purchasing unit can input the user's geographical location information into AI, and the AI ​​can select the optimal purchase method.

[0111] The purchasing department can analyze a user's social media activity and suggest purchasing methods at the time of purchase. For example, if a user frequently posts about a particular brand or store, the purchasing department will suggest purchasing methods from that brand or store. For example, if a user frequently posts reviews of a particular product, the purchasing department will prioritize suggesting that product. For example, if a user plans to attend a particular event, the purchasing department will suggest purchasing methods for products related to that event. In this way, by analyzing a user's social media activity, the optimal purchasing method can be suggested. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input user social media activity data into AI, and the AI ​​can suggest purchasing methods.

[0112] The generation unit can estimate the user's emotions and adjust the wrapping method based on the estimated emotions. For example, if the user is emotionally unstable, the generation unit may suggest a simple and calm wrapping design. If the user is relaxed, for example, the generation unit may suggest a bright and cheerful wrapping design. If the user is in a hurry, for example, the generation unit may suggest a method that allows for quick wrapping. By adjusting the wrapping method based on the user's emotions, more appropriate wrapping can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an AI, which can then adjust the wrapping method.

[0113] The generation unit can select the optimal wrapping method by referring to the user's past wrapping history when generating wrapping. For example, the generation unit may suggest a similar design based on a wrapping design the user has previously preferred. For example, the generation unit may suggest similar materials based on wrapping materials the user has previously used. For example, the generation unit may suggest a wrapping method suitable for a specific event based on the user's past wrapping history. In this way, the optimal wrapping method can be selected by referring to the user's past wrapping history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past wrapping history data into AI, and the AI ​​can select the optimal wrapping method.

[0114] The generation unit can customize the wrapping based on the user's current hobbies and preferences when generating the wrapping. For example, if the user has started a new hobby, the generation unit will suggest a wrapping design related to that hobby. For example, if the user likes a particular color, the generation unit will suggest a wrapping based on that color. For example, if the user likes a particular theme, the generation unit will suggest a wrapping based on that theme. This allows for more personalized wrapping by customizing the wrapping based on the user's current hobbies and preferences. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's current hobbies and preferences into the AI, which can then customize the wrapping.

[0115] The generation unit can estimate the user's emotions and determine wrapping priorities based on the estimated emotions. For example, if the user is emotionally unstable, the generation unit will prioritize simple and calming wrapping designs. If the user is relaxed, the generation unit will prioritize bright and cheerful wrapping designs. If the user is in a hurry, the generation unit will prioritize methods that allow for quick wrapping. This allows for more appropriate wrapping by determining wrapping priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can then determine wrapping priorities.

[0116] The generation unit can select the optimal wrapping method by considering the user's geographical location information when generating wrapping. For example, if the user is in a specific region, the generation unit will suggest wrapping that is appropriate for the culture and customs of that region. For example, if the user is traveling, the generation unit will suggest wrapping that is suitable for receiving the item at their travel destination. For example, if the user is planning to attend a specific event, the generation unit will suggest wrapping that is related to that event. In this way, the optimal wrapping method can be selected by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into the AI, and the AI ​​can select the optimal wrapping method.

[0117] The generation unit can analyze the user's social media activity and suggest wrapping options when generating wrapping. For example, if the user frequently posts about a particular design or theme, the generation unit will suggest wrapping based on that design or theme. For example, if the user plans to attend a particular event, the generation unit will suggest wrapping related to that event. For example, if the user frequently posts about a particular brand or store, the generation unit will suggest wrapping for that brand or store. In this way, by analyzing the user's social media activity, the optimal wrapping option can be suggested. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into AI, which can then suggest wrapping options.

[0118] The explanation unit can estimate the user's emotions and adjust its explanation based on those emotions. For example, if the user is emotionally unstable, the explanation unit will use gentle language. If the user is relaxed, the explanation unit will provide a detailed explanation. If the user is in a hurry, the explanation unit will provide a concise and to-the-point explanation. By adjusting the explanation based on the user's emotions, a more appropriate explanation can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation unit may be performed using AI or not. For example, the explanation unit can input user emotion data into an AI, which can then adjust its explanation.

[0119] The explanation unit can select the optimal explanation method by referring to the user's past explanation history during explanation. For example, the explanation unit can explain using a similar method based on the explanation method the user has preferred in the past. For example, the explanation unit can explain using a similar method based on the explanation method the user has found easy to understand in the past. For example, the explanation unit can suggest an explanation method suitable for a specific event based on the user's past explanation history. In this way, the optimal explanation method can be selected by referring to the user's past explanation history. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's past explanation history data into AI, and the AI ​​can select the optimal explanation method.

[0120] The explanation unit can customize its explanations based on the user's current knowledge level. For example, if the user is a beginner, the explanation unit will focus on basic information. If the user is an intermediate user, the explanation unit will include detailed information. If the user is an advanced user, the explanation unit will include specialized information. This allows for more appropriate explanations by customizing them based on the user's current knowledge level. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input user knowledge level data into AI, which can then customize the explanation.

[0121] The explanation unit can estimate the user's emotions and determine the priority of explanations based on the estimated emotions. For example, if the user is emotionally unstable, the explanation unit will prioritize explaining important information. For example, if the user is relaxed, the explanation unit will prioritize explanations containing detailed information. For example, if the user is in a hurry, the explanation unit will prioritize concise and to-the-point explanations. This allows for more appropriate explanations by determining the priority of explanations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation unit may be performed using AI or not using AI. For example, the explanation unit can input user emotion data into an AI, which can then determine the priority of explanations.

[0122] The explanation unit can select the most appropriate explanation method by considering the user's geographical location information during the explanation process. For example, if the user is in a specific region, the explanation unit will provide an explanation tailored to the culture and customs of that region. For example, if the user is traveling, the explanation unit will provide an explanation suitable for receiving the product at their travel destination. For example, if the user is planning to attend a specific event, the explanation unit will provide an explanation related to that event. In this way, the explanation unit can select the most appropriate explanation method by considering the user's geographical location information. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the user's geographical location information into AI, which can then select the most appropriate explanation method.

[0123] The explanation unit can analyze the user's social media activity and suggest explanation methods during the explanation process. For example, if the user frequently posts about a particular topic, the explanation unit will provide an explanation related to that topic. For example, if the user belongs to a particular group, the explanation unit will provide an explanation related to that group. For example, if the user plans to attend a particular event, the explanation unit will provide an explanation related to that event. In this way, by analyzing the user's social media activity, the explanation unit can suggest the most suitable explanation method. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can input the user's social media activity data into AI, and the AI ​​can suggest explanation methods.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The gift suggestion system can estimate the user's emotions and customize gift suggestions based on those emotions. For example, if the user is emotionally unstable, it can suggest a gift with a relaxing effect. If the user is relaxed, it can suggest a gift related to their hobbies. Also, if the user is in a hurry, it can suggest a gift that can be obtained quickly. This allows the system to suggest the most suitable gift based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then customize gift suggestions.

[0126] The gift suggestion system can analyze a user's past purchase history and suggest the most suitable gift. For example, it can analyze the trends of products a user has purchased in the past and suggest similar products. If a user prefers a particular brand, it can suggest products from that brand. Also, if a user frequently purchases products from a particular category, it can suggest products from that category. In this way, the system can suggest the most suitable gift based on the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past purchase history data into the AI, which can then suggest the most suitable gift.

[0127] The gift suggestion system can customize gift suggestions based on the user's current lifestyle. For example, if a user starts a new hobby, the system can suggest gifts related to that hobby. If a user moves, the system can suggest gifts related to their new area. Furthermore, if a user plans to attend a specific event, the system can suggest gifts related to that event. This allows the system to suggest the most suitable gift based on the user's current lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's current lifestyle data into the AI, which can then customize the gift suggestions.

[0128] The gift suggestion system can estimate the user's emotions and adjust the gift wrapping method based on those emotions. For example, if the user is emotionally unstable, it can suggest a simple and calm wrapping design. If the user is relaxed, it can suggest a vibrant and cheerful wrapping design. If the user is in a hurry, it can suggest a method that allows for quick wrapping. This allows the system to suggest the optimal wrapping method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the AI, which can then adjust the wrapping method.

[0129] The gift suggestion system can suggest gifts while considering the user's geographical location. For example, if the user is in a specific region, it can suggest gifts that are appropriate for the culture and customs of that region. If the user is traveling, it can suggest gifts that are suitable for receiving at their destination. Furthermore, if the user is planning to attend a specific event, it can suggest gifts related to that event. This allows the system to suggest the most suitable gift while considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into the AI, which can then suggest the most suitable gift.

[0130] The gift suggestion system can estimate the user's emotions and adjust the way it describes the gift based on those emotions. For example, if the user is emotionally unstable, it can use gentle language in its description. If the user is relaxed, it can provide a more detailed description. If the user is in a hurry, it can provide a concise and to-the-point description. This allows the system to suggest the most appropriate description based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may include, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the processing described above in the description unit may be performed using AI or not. For example, the description unit can input user emotion data into the AI, which can then adjust the description.

[0131] The gift suggestion system can analyze a user's social media activity and suggest the most suitable gift. For example, if a user frequently posts about a particular topic, it can suggest a gift related to that topic. If a user belongs to a particular group, it can suggest a gift related to that group. Furthermore, if a user plans to attend a particular event, it can suggest a gift related to that event. This allows the system to analyze a user's social media activity and suggest the most suitable gift. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's social media activity data into an AI, which can then suggest the most suitable gift.

[0132] The gift suggestion system can estimate the user's emotions and prioritize gifts based on those emotions. For example, if the user is emotionally unstable, it can prioritize suggesting gifts that will have a positive effect. If the user is relaxed, it can prioritize suggesting gifts related to their hobbies. If the user is in a hurry, it can prioritize suggesting gifts that can be obtained quickly. This allows the system to determine the optimal gift priority based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then determine the gift priority.

[0133] The gift suggestion system can analyze a user's past social media activity and select the most suitable gift suggestion method. For example, it can analyze the times when a user frequently posts and suggest gifts during those times. If a user frequently uses a particular hashtag, it can prioritize suggesting gifts related to that hashtag. Furthermore, if a user frequently uses a particular social media platform, it can suggest gifts based on information from that platform. This allows the system to analyze a user's past social media activity and select the most suitable gift suggestion method. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past social media activity data into an AI, which can then select the most suitable gift suggestion method.

[0134] The gift suggestion system can estimate the user's emotions and adjust the timing of gift purchases based on those emotions. For example, if the user is emotionally unstable, the purchase timing can be delayed. If the user is relaxed, the purchase timing can be accelerated. Also, if the user is in a hurry, the purchase can be made quickly. This allows for the optimal purchase timing to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the purchase section may be performed using AI or not. For example, the purchase section can input user emotion data into AI, and the AI ​​can adjust the purchase timing.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit collects user information. For example, the collection unit collects the user's SNS registration information and initial registration information. Step 2: The suggestion department proposes gifts and timing based on the information collected by the data collection department. For example, the suggestion department analyzes data such as the user's past purchase history, hobbies, and interests to select the most suitable gift. Step 3: The analysis unit predicts the optimal timing for purchasing the gift suggested by the proposal unit. For example, the analysis unit analyzes sales information from e-commerce sites to predict the best time to purchase. Step 4: The purchasing unit purchases the product at the timing predicted by the analysis unit. For example, the purchasing unit predicts when a particular product will go on sale and makes a purchase at that time. Step 5: The generation unit wraps the products purchased by the purchase unit. For example, the generation unit generates gift wrapping methods tailored to the individual's tastes and preferences. Step 6: The description section describes the wrapping and message card generated by the generation section. The description section describes, for example, the selected gift item and its meaning.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, proposal unit, analysis unit, purchase unit, generation unit, and explanation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's SNS registration information and initial registration information using the control unit 46A of the smart device 14. The proposal unit analyzes data such as the user's past purchase history, hobbies, and interests using the specific processing unit 290 of the data processing unit 12 to select the most suitable gift. The analysis unit analyzes sales information from e-commerce sites using the specific processing unit 290 of the data processing unit 12 to predict the optimal purchase timing. The purchase unit makes a purchase when a specific product goes on sale using the control unit 46A of the smart device 14. The generation unit generates gift wrapping methods tailored to the individual's hobbies and preferences using the control unit 46A of the smart device 14. The explanation unit explains the selected gift product and its intent using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the collection unit, proposal unit, analysis unit, purchase unit, generation unit, and explanation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's SNS registration information and initial registration information using the control unit 46A of the smart glasses 214. The proposal unit analyzes data such as the user's past purchase history, hobbies, and interests using the specific processing unit 290 of the data processing unit 12 to select the most suitable gift. The analysis unit analyzes sales information from e-commerce sites using the specific processing unit 290 of the data processing unit 12 to predict the optimal purchase timing. The purchase unit makes a purchase when a specific product goes on sale using the control unit 46A of the smart glasses 214. The generation unit generates a gift wrapping method tailored to the individual's hobbies and preferences using the control unit 46A of the smart glasses 214. The explanation unit explains the selected gift product and its intent using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the collection unit, proposal unit, analysis unit, purchase unit, generation unit, and explanation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's SNS registration information and initial registration information using the control unit 46A of the headset terminal 314. The proposal unit analyzes data such as the user's past purchase history, hobbies, and interests using, for example, the specific processing unit 290 of the data processing unit 12, and selects the most suitable gift. The analysis unit analyzes sales information from e-commerce sites using, for example, the specific processing unit 290 of the data processing unit 12, and predicts the optimal timing for purchase. The purchase unit makes a purchase when a specific product goes on sale using, for example, the control unit 46A of the headset terminal 314. The generation unit generates gift wrapping methods tailored to the individual's hobbies and preferences using, for example, the control unit 46A of the headset terminal 314. The explanation unit explains the selected gift product and intention using, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0180] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0182] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the collection unit, proposal unit, analysis unit, purchase unit, generation unit, and explanation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's SNS registration information and initial registration information by the control unit 46A of the robot 414. The proposal unit analyzes data such as the user's past purchase history, hobbies, and interests by the specific processing unit 290 of the data processing unit 12 and selects the most suitable gift. The analysis unit analyzes sales information on e-commerce sites by the specific processing unit 290 of the data processing unit 12 and predicts the optimal purchase timing. The purchase unit makes a purchase when a specific product goes on sale by, for example, the control unit 46A of the robot 414. The generation unit generates a gift wrapping method tailored to the individual's hobbies and preferences by, for example, the control unit 46A of the robot 414. The explanation unit explains the selected gift product and its intent by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0192] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0193] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0194] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0199] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0200] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0201] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0202] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0203] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) A collection unit that collects user information, Based on the information collected by the aforementioned collection unit, a proposal unit proposes gifts and timings. An analysis unit predicts the timing of purchasing the gift proposed by the aforementioned proposal unit, A purchasing unit that purchases the product at the timing predicted by the analysis unit, A production unit that wraps the products purchased by the aforementioned purchasing unit, The system includes an explanatory section that describes the wrapping and message card generated by the generation section. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects users' SNS registration information and initial registration information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the information collected by the aforementioned collection unit, the system proposes gifts and their timing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We analyze sales information from e-commerce sites and predict the optimal time to buy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate gift wrapping methods tailored to individual tastes and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate message card text The system described in Appendix 1, characterized by the features described herein. (Note 7) The above explanatory section is, Explain the selected gift items and the intention behind them. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past social media activity and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the gift. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the gift category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the gifts will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the gifts. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, past sales information is referenced to predict the optimal purchase timing. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, different analysis methods are applied to each category of gift. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, the geographical distribution of the gifts will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During the analysis, we refer to relevant literature on gifts to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned purchasing department, It estimates the user's emotions and adjusts the timing of purchases based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned purchasing department, At the time of purchase, the system analyzes the user's past purchase history to select the optimal purchase method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned purchasing department, At the time of purchase, the purchase method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned purchasing department, It estimates user emotions and determines purchase priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned purchasing department, When making a purchase, the system will select the most suitable purchase method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned purchasing department, When a user makes a purchase, we analyze their social media activity and suggest ways to make that purchase. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is It estimates the user's emotions and adjusts the wrapping method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating a wrapping, the system selects the optimal wrapping method by referring to the user's past wrapping history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is When generating the wrapping, customize the wrapping based on the user's current tastes and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is It estimates the user's emotions and determines wrapping priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is When generating the wrapping, the optimal wrapping method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The generating unit is When generating gift wrapping, the system analyzes the user's social media activity and suggests gift wrapping options. The system described in Appendix 1, characterized by the features described herein. (Note 38) The above explanatory section is, It estimates the user's emotions and adjusts the explanation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The above explanatory section is, During the explanation, the system selects the most suitable explanation method by referring to the user's past explanation history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The above explanatory section is, When explaining something, customize the explanation based on the user's current knowledge level. The system described in Appendix 1, characterized by the features described herein. (Note 41) The above explanatory section is, It estimates the user's emotions and determines the priority of explanations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The above explanatory section is, During the explanation, the optimal explanation method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The above explanatory section is, During the explanation, we analyze the user's social media activity and propose methods for explanation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user information, Based on the information collected by the aforementioned collection unit, a proposal unit proposes gifts and timings. An analysis unit predicts the timing of purchasing the gift proposed by the aforementioned proposal unit, A purchasing unit that purchases the product at the timing predicted by the analysis unit, A production unit that wraps the products purchased by the aforementioned purchasing unit, The system includes an explanatory section that describes the wrapping and message card generated by the generation section. A system characterized by the following features.

2. The aforementioned collection unit is Collects users' SNS registration information and initial registration information. The system according to feature 1.

3. The aforementioned proposal section is, Based on the information collected by the aforementioned collection unit, the system proposes gifts and their timing. The system according to feature 1.

4. The aforementioned analysis unit, We analyze sales information from e-commerce sites and predict the optimal time to buy. The system according to feature 1.

5. The generating unit is Generate gift wrapping methods tailored to individual tastes and preferences. The system according to feature 1.

6. The generating unit is Generate message card text The system according to feature 1.

7. The above explanatory section is, Explain the selected gift items and the intention behind them. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past social media activity and select the optimal information gathering method. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

Images